(6) #206 Kenyon (6-11)

725.52 (1)

Click on column to sort  • 
# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
231 Messiah Loss 10-13 -21.63 18 5.26% Counts Mar 5th FCS D III Tune Up
87 Richmond Loss 6-13 -5.8 26 5.26% Counts (Why) Mar 5th FCS D III Tune Up
74 Navy Loss 7-13 -0.32 37 5.26% Counts Mar 5th FCS D III Tune Up
181 Davidson Loss 9-11 -8.24 20 5.26% Counts Mar 5th FCS D III Tune Up
174 Rochester Loss 2-13 -26.41 52 5.26% Counts (Why) Mar 6th FCS D III Tune Up
110 Christopher Newport Loss 7-13 -9.38 25 5.26% Counts Mar 6th FCS D III Tune Up
50 Berry Loss 8-13 12.67 19 5.26% Counts Mar 6th FCS D III Tune Up
324 Xavier Win 13-5 5.16 8 7.44% Counts (Why) Apr 16th Ohio D III College Mens CC 2022
99 Oberlin Loss 10-12 15.78 115 7.44% Counts Apr 16th Ohio D III College Mens CC 2022
271 Cedarville Win 13-4 29.95 12 7.44% Counts (Why) Apr 17th Ohio D III College Mens CC 2022
357 Ohio Wesleyan** Win 13-2 0 8 0% Ignored (Why) Apr 17th Ohio D III College Mens CC 2022
214 Wooster Loss 8-13 -41.76 13 7.44% Counts Apr 17th Ohio D III College Mens CC 2022
271 Cedarville Loss 12-15 -48.11 12 8.35% Counts Apr 30th Ohio Valley D III College Mens Regionals 2022
219 Swarthmore Win 15-9 44.12 16 8.35% Counts Apr 30th Ohio Valley D III College Mens Regionals 2022
99 Oberlin Loss 11-14 11.03 115 8.35% Counts Apr 30th Ohio Valley D III College Mens Regionals 2022
236 Shippensburg Win 15-8 44.84 24 8.35% Counts (Why) May 1st Ohio Valley D III College Mens Regionals 2022
358 Lehigh-B** Win 15-2 0 36 0% Ignored (Why) May 1st Ohio Valley D III College Mens Regionals 2022
**Blowout Eligible. Learn more about how this works here.

FAQ

The results on this page ("USAU") are the results of an implementation of the USA Ultimate Top 20 algorithm, which is used to allocate post season bids to both colleg and club ultimate teams. The data was obtained by scraping USAU's score reporting website. Learn more about the algorithm here. TL;DR, here is the rating function. Every game a team plays gets a rating equal to the opponents rating +/- the score value. With all these data points, we iterate team ratings until convergence. There is also a rule for discounting blowout games (see next FAQ)
For reference, here is handy table with frequent game scrores and the resulting game value:
"...if a team is rated more than 600 points higher than its opponent, and wins with a score that is more than twice the losing score plus one, the game is ignored for ratings purposes. However, this is only done if the winning team has at least N other results that are not being ignored, where N=5."

Translation: if a team plays a game where even earning the max point win would hurt them, they can have the game ignored provided they win by enough and have suffficient unignored results.